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Climate Change Impact and Adaptation in Asian Coastal Cities A Joint Study by the World Bank, JICA and ADB.

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Presentation on theme: "Climate Change Impact and Adaptation in Asian Coastal Cities A Joint Study by the World Bank, JICA and ADB."— Presentation transcript:

1 Climate Change Impact and Adaptation in Asian Coastal Cities A Joint Study by the World Bank, JICA and ADB

2 Objective of the Study Strengthen the understanding of the economic, social and environmental impacts of climate variability and change Strengthen the understanding of the economic, social and environmental impacts of climate variability and change Identify what national and municipal decision makers should expect in the way of climate change impacts, and the scale of those impacts in coastal cities Identify what national and municipal decision makers should expect in the way of climate change impacts, and the scale of those impacts in coastal cities

3 Four Cities Bangkok (World Bank) Bangkok (World Bank) Ho Chi Minh City (ADB) Ho Chi Minh City (ADB) Kolkata (World Bank) Kolkata (World Bank) Manila (JICA) Manila (JICA)

4 Asian Mega-City Hotspots Source: Adapted from (WWF, 2009).

5 Study Framework Develop risk assessment modules for current conditions Develop risk assessment modules for current conditions Downscale climate change forecasts for 2050 Downscale climate change forecasts for 2050 Apply climate change parameters to hazard modules Apply climate change parameters to hazard modules Assess change in risk Assess change in risk Examine adaptation measures to reduce risks Examine adaptation measures to reduce risks

6 Risk assessment process Hazard module Hazard module Event frequency relationshipsEvent frequency relationships Hydro-meteorological modelsHydro-meteorological models Exposure module (assets at risk) Exposure module (assets at risk) Vulnerability module (event-damage) Vulnerability module (event-damage) Damage module (damage-loss) Damage module (damage-loss) Loss module Loss module Loss exceedance relationshipLoss exceedance relationship Expected annual loss (EAL)Expected annual loss (EAL)

7 Climate Change Scenarios Study examined the A1FI and the B1 scenarios as likely high-low cases Study examined the A1FI and the B1 scenarios as likely high-low cases Used 2050 as the time horizon, which is in line with time frame needed for major flood protection planning/investments Used 2050 as the time horizon, which is in line with time frame needed for major flood protection planning/investments

8 Statistical Downscaling Bangkok and Manila city case studies used Statistical downscaling estimates provided by the Integrated Research System for Sustainability Science (IR3S) at the University of Tokyo Bangkok and Manila city case studies used Statistical downscaling estimates provided by the Integrated Research System for Sustainability Science (IR3S) at the University of Tokyo Mean temperature increases factors,Mean temperature increases factors, Precipitable water increase factors,Precipitable water increase factors, Extreme 24-hour precipitation increase factors,Extreme 24-hour precipitation increase factors, Seasonal mean precipitation increases factors.Seasonal mean precipitation increases factors. Masahiro Sugiyama, Final Report, Study on Climate Change Adaptation and Mitigation in Asian Coastal Mega-cities, Integrated Research System for Sustainability Science (IR3S) at the University of Tokyo, July 2008.

9 IR3S report highlights (1) There is a robust linear relationship between the local temperature increase in each target area and the global mean temperature increase There is a robust linear relationship between the local temperature increase in each target area and the global mean temperature increase Precipitable water in the four megacity areas increase at a rate of ~ 8%/ 0 K or larger Precipitable water in the four megacity areas increase at a rate of ~ 8%/ 0 K or larger For mean seasonal precipitation change, instead of analyzing precipitation change per temperature change, the study develop scenarios of seasonal mean precipitation by averaging monthly data of the IPCC climate model outputs For mean seasonal precipitation change, instead of analyzing precipitation change per temperature change, the study develop scenarios of seasonal mean precipitation by averaging monthly data of the IPCC climate model outputs

10 IR3S report highlights (2) For return periods larger than about 10 years, the the IPCC models projected extreme 24-hr precipitation change ranges from ~ 3%/ 0 K to ~ 28 %/ 0 K. The uncertainty in precipitation extremes is much larger than in temperature or precipitable water. For return periods larger than about 10 years, the the IPCC models projected extreme 24-hr precipitation change ranges from ~ 3%/ 0 K to ~ 28 %/ 0 K. The uncertainty in precipitation extremes is much larger than in temperature or precipitable water. To provide 24-hr extreme precipitation increases scenarios, the study used physical intuition. Some researchers argued that precipitation extremes should scale as the moisture availability in the atmosphere, that is, precipitable water (Allen and Ingram 2002; Trenberth et al. 2003). To provide 24-hr extreme precipitation increases scenarios, the study used physical intuition. Some researchers argued that precipitation extremes should scale as the moisture availability in the atmosphere, that is, precipitable water (Allen and Ingram 2002; Trenberth et al. 2003). Allen, M. R., and W. J. Ingram, 2002: Constrains on future changes in climate and the hydrologic cycle. Nature, 419, , doi: /nature01092; Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bulletin of the American Meteorological Society, 84, , doi: /BAMS

11 IR3S Results

12 Nested Regional Circulation Model HCMC Southeast Asia (SEA) Regional Center (RC) for the Systems for Analysis, Research and Training (START) undertook the work Southeast Asia (SEA) Regional Center (RC) for the Systems for Analysis, Research and Training (START) undertook the work Applied the PRECIS model nested in the low resolution (~2 o ) ECHAM Version 4 AOGCM model Applied the PRECIS model nested in the low resolution (~2 o ) ECHAM Version 4 AOGCM model Used the A2 and B2 scenarios Used the A2 and B2 scenarios

13 Comparison of seasonal rainfall

14 Bangkok City Case Study Panya Consultants headed study Panya Consultants headed study Bangkok lies in the Chao Phraya River Basin with area of 159,000 km 2 Bangkok lies in the Chao Phraya River Basin with area of 159,000 km 2 Tropical monsoon climate Tropical monsoon climate 1,130 mm average annual precipitation varying from 1,000 mm to 1,600 mm 1,130 mm average annual precipitation varying from 1,000 mm to 1,600 mm Flooding driven by high seasonal rainfall events over 2 to 3 months Flooding driven by high seasonal rainfall events over 2 to 3 months

15 Chao Phraya River Basin

16 Bangkok historic flooding 1995 flood caused serious damage and was estimated to have a 1/30 year return period 1995 flood caused serious damage and was estimated to have a 1/30 year return period Recent floods have occurred in 2002 and 2006 Recent floods have occurred in 2002 and 2006 Flood protection system is designed for 1/30-yr flood. There is a surrounding embankment and pumped drainage Flood protection system is designed for 1/30-yr flood. There is a surrounding embankment and pumped drainage

17 Chao Phraya Basin and Bangkok Hydrologic Simulation Model Schematic

18 2050 Climate Change Factors

19 Bangkok Climate Change Impact – Hazard Module Adjustments Mean seasonal increases were applied to the 1/10-yr, 1/30-yr, and 1/100-yr precipitation events and distributed across the watershed spatially and temporally using historical rainfall distribution patterns (1995) Mean seasonal increases were applied to the 1/10-yr, 1/30-yr, and 1/100-yr precipitation events and distributed across the watershed spatially and temporally using historical rainfall distribution patterns (1995) SLR applied to hydrologic model boundary conditions SLR applied to hydrologic model boundary conditions Land subsidence applied to digital elevation maps Land subsidence applied to digital elevation maps

20 Maximum water depth for 1/30-yr flood 2008 and 2050 A1FI

21 Increasing Hazard

22 Direct and indirect losses 1/30-yr A1FI Scenarion

23 Loss exceedance curve

24 Expected annual loss (mn Baht)

25 Summary of Bangkok Findings The economic damage of flooding will rise roughly four-fold in The economic damage of flooding will rise roughly four-fold in % of the cost in 2050 would be attributed to land subsidence alone. 70% of the cost in 2050 would be attributed to land subsidence alone. About one million inhabitants of Bangkok and Samut Prakarn will be affected by the A1FI climate change condition in About one million inhabitants of Bangkok and Samut Prakarn will be affected by the A1FI climate change condition in One in eight of the affected inhabitants will be from the condensed housing areas where most live below the poverty level. One in eight of the affected inhabitants will be from the condensed housing areas where most live below the poverty level. One-third of the total affected people may be subjected to more than a half-meter inundation for at least one week. One-third of the total affected people may be subjected to more than a half-meter inundation for at least one week.

26 Adaptation Options

27 Adaptation benefits = Losses Avoided The expected annual benefit with an adaptation project is the difference in the area between flood damage cost curves without and with the project. The expected annual benefit with an adaptation project is the difference in the area between flood damage cost curves without and with the project. Adaptation Benefits = EAL Without Project – EAL With Project Adaptation Benefits = EAL Without Project – EAL With Project

28 Contact Points Synthesis Report: Poonam Pillai, Bangkok City Case Study: Jan Bojo, Manila City Case Study Megumi Muto, HCMC City Case Study Jay Roop,

29 Thank you!


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